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Inferring contextual information such as demographics from historical transactions is valuable to public agencies and businesses. Existing methods are data-hungry and do not work well when the available records of transactions are sparse. We consider here specifically inference of demographic information using limited historical grocery transactions from a few random trips that a typical business or public service organization may see. We propose a novel method called DemoMotif to build a network model from heterogeneous data and identify subgraph patterns (i.e., motifs) that enable us to infer demographic attributes. We then design a novel motif context selection algorithm to find specific node combinations significant to certain demographic groups. Finally, we learn representations of households using these selected motif instances as context, and employ a standard classifier (e.g., SVM) for inference. For evaluation purposes, we use three real-world consumer datasets, spanning different regions and time periods in the U.S. We evaluate the framework for predicting three attributes: ethnicity, seniority of household heads, and presence of children. Extensive experiments and case studies demonstrate that DemoMotif is capable of inferring household demographics using only a small number (e.g., fewer than 10) of random grocery trips, significantly outperforming the state-of-the-art.

The interaction of hyperthermal oxygen atoms with graphitic carbon surfaces is of fundamental interest due to its importance in aerospace material degradation and in nanomaterials and nanodevice fabrication by plasma etching. In this work, we report the results of O(3P) atomic beam scattering from highly oriented pyrolytic graphite with incidence kinetic energies in the range 0.7–1.2 eV. Well-characterized incident beams of O(3P) are generated by VUV photolysis of carbon monoxide by a free-electron laser, and the scattering kinetic energy and angular distributions are measured by off-axis ion imaging. The results are used to benchmark an incoming/outgoing correlation ion imaging technique that is performed using a much simpler discharge source to generate O(3P) beams. By using the velocity information available from ion imaging to correlate the departure time of each scattered atom from the surface with the arrival time of atoms with a particular range of incidence velocities, the technique can achieve an incidence kinetic energy resolution much higher than the kinetic energy distribution of the incident beam. Good agreement is achieved between the two experimental approaches up to incidence energies of 1.0 eV, as long as the discharge source is used under conditions where background signal is kept low. The results are in qualitative agreement with trajectory simulations performed on a machine-learned spin-constrained triplet potential energy surface, which suggests that under the conditions used in this study, direct scattering takes place with limited triplet-to-singlet spin relaxation at the surface.

In extreme environments, such as polar oceans, where potential hazards like sea ice are prevalent, deploying autonomous surface vessel (ASV) can enhance operational efficiency and safeguard personnel. As these extreme environments necessitate higher performance standards, particularly in terms of path-following accuracy and control stability, we introduce in this research an ASV path-following control method predicated on an enhanced proportional–integral–derivative (PID) parameters tuning algorithm aimed at reducing path-following errors and bolstering control stability. First, the adaptive line-of-sight (ALOS) guidance algorithm is devised to determine the desired ASV heading by designing the forward-looking range adjustment strategy. Second, the improved sparrow search algorithm (ISSA) is proposed for PID parameters tuning. Since the lack of stability of the standard Sparrow Search Algorithm (SSA), the producer update strategy is modified, and the Brown–Levy mutation strategy is designed to improve the global search ability of the algorithm. Finally, the virtual ASV simulation platform is built, and the real-time PID controller is constructed by designing the PID real-time tuning strategy. The parameters of the Nomoto ship motion model are fitted in the simulation platform according to different marine environments, and the PID controller parameters are updated in real-time by ISSA to improve the path following accuracy. Experimental results of the marine environment simulation test and the real-world experiment show that the ALOS guidance algorithm can effectively generate the current desired rudder angle. The PID controller based on ISSA has the best performance in computer simulation. The average overshoot is 2.79%, and the average convergence time is 20.1 s. In the real-world experiment, the average path following error of ISSA Real-Time is reduced by 51.0% compared with that of SSA and 27.2% compared with that of ISSA. The improved control method can better satisfy the control requirements of the ASV, enhance control stability, and achieve more precise path following.

Efficient recovery of palladium (Pd) from waste sources is of paramount importance due to its limited natural reserves and potential environmental hazards. Herein, a carbon sorbent, namely sulfur-functionalized porous carbon microspheres (SPCMs), was used to selectively capture Pd(ii) from acidic solution. SPCMs exhibited high efficiency for the adsorption separation of Pd(ii) from 0.5 M to 6 M HNO3solution. The adsorption kinetics of Pd(ii) matched well with the pseudo-second-order model. The adsorption reached equilibrium after 130 minutes and the adsorption capacity of Pd(ii) was 79.3 mg g−1in 1 M HNO3solution. The Freundlich isotherm model exhibited a better description of the Pd(ii) adsorption, suggesting that the Pd(ii) adsorption is a multilayer adsorption process. SPCMs showed a high selectivity for the capture of Pd(ii) in simulated acidic wastewater with 26 metal ions, and the selectivity increased with the increase of HNO3concentration. The adsorption capacity per US dollar of Pd(ii) by SPCMs from HNO3solution is much higher than those of previously reported sorbents, exhibiting a high economic viability of SPCMs for Pd(ii) capture from acidic solution. The adsorbed Pd(ii) could be desorbed using 1.0 M thiourea and 0.1 M HNO3, and the SPCM sorbent maintained a high adsorption capacity after five adsorption–desorption cycles. Characterization and theoretical calculations revealed that the adsorption of Pd(ii) on the SPCM sorbent is dominated by the coordination of [Pd(NO3)2] with O/S containing groups and some of the Pd(ii) is reduced to Pd(0). The excellent adsorption performance of SPCMs provides a feasible and low-cost strategy for the selective recovery of Pd(ii) from acidic wastewater.

The single nondestructive testing (NDT) techniques face the problem of low resolution when simultaneously detecting different types and locations of defects. Composite inspection methods, which combine different NDT techniques, have been widely studied due to their high defect detection accuracy. However, most of the existing composite detection methods require complex sensor systems or rely on complex signal-processing algorithms. Therefore, this article proposes a novel hybrid sensor based on the electromagnetic acoustic transducer (EMAT) and the magnetic flux leakage (MFL) mechanism, which allows to accurately detect defects in ferromagnetic materials. This hybrid sensor employs a straightforward EMAT-MFL configuration, which only requires a single permanent magnet to generate the requisite magnetic field for the EMAT and MFL sensor components. In addition, it adopts a unique orthogonal butterfly coil design for the detection of cracks. The obtained results show a significant frequency difference between the EMAT and MFL signals, which demonstrates their independence and lack of interference. This eliminates the potential issue of signal aliasing decoupling. The hybrid sensor can fully use the advantages of the EMAT and MFL technologies to simultaneously detect defects and cracks on the top and bottom surfaces of ferromagnetic materials. Furthermore, the wall thinning defects can be detected with a maximum detection error of only 4.48%. This article proposes a feasible approach for miniaturizing hybrid sensors and increasing their detection performance.

Gas turbine blades are critical components that operate under extreme conditions, requiring precise manufacturing techniques to ensure their mechanical performance and durability. To improve the quality of gas turbine blades manufactured by investment casting, numerical simulations were employed to study the effects of shell preheating temperature and pouring temperature on the filling and solidification processes of casting. The simulation results indicate that both higher and lower shell temperatures adversely affect blade casting quality. When the shell preheating temperature is 1150°C or 1100°C, the castings exhibit significant localized temperature retention in the thick regions. At 1050°C, heat transfers from the thin-walled sections with higher temperature to the thick-walled sections with lower temperature, exacerbating the uneven cooling in the thick regions. At 1000°C, the cooling rate of the thick and thin regions is nearly the same, reducing internal casting defects. Additionally, the relationship between pouring temperature and filling status is not a simple linear one but is instead influenced by the interaction of multiple factors. The optimized preheating temperature of shell mold and pouring temperature are 1000°C and 1505°C, respectively, and the defects are largely eliminated.

Visually monitoring micro-crack initiation and corrosion failure evolution is crucial for early diagnosis of structural health and ensuring safe operation of infrastructures. However, existing damage detecting approaches are subject to the limited-detection of heterogeneous structures, intolerance of harsh environments, and challenge of quantitative analysis, impeding applications in structural health monitoring (SHM). Herein, we present a stretchable, semi-quantitative, instrument-free, supramolecular SHM sensor by integrating a polyurea elastomer with sensitive corrosion-probes, enabling localized corrosion monitoring and quantification of failure dynamics. Initially, a correlation between visual monitoring signals and structural health status is proposed, and sensor-based image processing software that accurately quantifies structural failure indicators (crack scale, corrosion reactivity and deterioration status) is proposed. Moreover, this sensor can be fabricated as multiple derivatives: a coating or patch covered on metallic substrates and an ionic-responsive test strip, ensuring real-time detection of the initiation of pitting, degradation events of metallic components and convenient monitoring of ion concentrations in corrosive media. Furthermore, the inherent geometric plasticity and dynamic hydrogen-bonded network validates the reliability for heterogeneous components and stability under extreme environments of sensors. This portable, smart SHM strategy established the channel-transformation model from corrosion dynamics to visual signals, exhibiting prospects for structural monitoring in offshore energy-harvesting equipment.

We report an efficient, metal-free method for late-stage rapid [18F]trifluoromethyl radiolabeling of terminal alkenes at room temperature. Utilizing 3,3-difluoroallyl sulfonium salts as precursors, the process achieves high radiochemical yields (up to 94 ± 2%) in just 30 s, with excellent functional group tolerance. This method offers a simplified and efficient pathway to produce [18F]trifluoromethylated terminal alkene compounds, enabling their application in PET imaging and expanding the chemical space for drug discovery.

Single-atom catalysts (SACs) are extensively applied in Fenton-like catalytic processes to treat water pollutants. However, the role of the porous structures of SACs supports in catalytic reactions is often overlooked despite its significant contribution to mass diffusion during the reaction. Herein, we adopted a hard-template-assisted approach to fabricate Fe-based SACs (Fe-SACs) featuring a mesoporous architecture. The SiO2template not only adjusts the pore architecture of the support but also facilitates the conversion of active sites from nanoscale sites to single-atom sites, thereby improving the selectivity for pollutant degradation via nonradical pathways (singlet oxygen and electron transfer mechanism). The experimental results demonstrated that using large-sized SiO2(∼200 nm) as a template leads to metal aggregation on its surface, forming Fe nanoparticles (Fe-NPs). Fe-NPs exhibit narrow pore structures that prevent peroxymonosulfate (PMS) from being activated, resulting in a slow degradation of pollutants primarily through radical pathways. In contrast, employing small-sized SiO2(∼10 nm) as a hard template not only produces supports with mesoporous structures but also promotes the building of single-atom active sites. The prepared Fe-SACs effectively activated PMS through nonradical pathways and removed contaminants at a rate kof 0.89 min–1, 33 times faster than Fe-NPs. This template-assisted method sheds light on the synthesis of effective Fenton-like catalysts with porous structures that enhance the efficient breakdown of contaminants in wastewater.

The accurate and fast positioning of DIE chips is crucial for conducting high and low temperature online performance tests during the manufacturing process of chips. The original online performance test equipment for DIE chips cannot accurately locate polluted chips, resulting in serious missing and false detections. To address this issue, this paper proposes a robust positioning method based on semantic segmentation network for DIE chips. Unet is used to segment the two circular electrodes of the DIE chip. After that, image processing techniques, such as contour extraction and circle fitting, are employed to extract the precise location of the DIE chip. To satisfy the requirements of fast and precise positioning, improved strategies such as modifying convolutional blocks, compressing the channels of convolutional layers, the weighted combination of the dice and the cross-entropy loss function, and so on are adopted to improve Unet (named IUnet). Additionally, to address the issue of insufficient samples for the low temperature performance test, model-based transfer learning is employed to improve the segmentation accuracy of IUnet. The experimental results show that the proposed method, compared with mainstream methods such as traditional methods (template matching and Hough circle detection), target detection methods (SSD, YoloV7, and FasterRCNN), and semantic segmentation methods (SegNet, DeeplabV3+, Unet, Res-Unet++, and Swin-Unet), has better positioning performance and robustness. Under the premise of achieving a positioning beat of 0.5 s per chip, the positioning accuracy rate of DIE chips can reach 100 %, thereby satisfying the positioning requirements of the enterprise.

Data sharing in Vehicular Social Networks (VSNs) is an essential road service that assists vehicle driving and promotes intelligent transportation applications. In VSNs, vehicles regularly collect and upload valuable data to share with other vehicles. Data encryption can be employed during data uploading and sharing to prevent malicious tampering and privacy disclosure. However, existing data-sharing schemes lack security, have high overhead in obtaining decrypted data, and show low trust in the central authority controlling the entire network. To facilitate data sharing in VSNs, this paper proposes a new scheme using consortium blockchain to realize secure data sharing. Nodes in the blockchain invoke smart contracts and implement the location-based Speculative Byzantine Fault Tolerance (LSBFT) to accomplish data-sharing transactions among vehicles. The scheme not only ensures the security of vehicle information but also protects the privacy of the shared data. Security analysis demonstrates that the proposed scheme can resist attacks and has shown transaction fairness, data confidentiality, non-repudiation, and traceability. Simulation results show that the scheme has higher sharing efficiency and less time to reach a consensus in the data storage process.

The implementation of BESS (battery energy storage systems) and the efficient optimization of their scheduling are crucial research challenges in effectively managing the intermittency and volatility of solar-PV (photovoltaic) systems. Nevertheless, an examination of the existing body of knowledge uncovers notable deficiencies in the ideal arrangement of energy systems' timetables. Most models primarily concentrate on a single aim, whereas only a few tackle the intricacies of multi-objective scenarios. This study examines homes connected to the power grid equipped with a BESS and a solar PV system. It leverages four distinct reinforcement learning (RL) algorithms, selected for their unique training methodologies, to develop effective scheduling models. The findings demonstrate that the RL model using Trust Region Policy Optimization (TRPO) effectively manages the BESS and PV system despite real-world uncertainties. This case study confirms the suitability and effectiveness of this approach. The TRPO-based RL framework surpasses previous models in decision-making by choosing the most optimal BESS scheduling strategies. The TRPO model exhibited the highest mean self-sufficiency rates compared to the A3C (Asynchronous Advantage Actor-Critic), DDPG (Deep Deterministic Policy Gradient), and TAC (Twin Actor Cretic) models, surpassing them by ∼ 3 %, 0.72 %, and 3.5 %, correspondingly. This results in enhanced autonomy and economic benefits by adapting to dynamic real-world conditions. Consequently, our approach was strategically designed to deliver an optimized outcome. This framework is primarily intended for seamless integration into an automated energy plant environment, facilitating regular electricity trading among multiple buildings. Backed by initiatives like the Renewable Energy Certificate weight, this technology is expected to play a crucial role in maintaining a balance between power generation and consumption. The MILP (Mixed Integer Linear Programming) architecture achieved a self-sufficiency rate of 29.12 %, surpassing the rates of A3C, TRPO, DDPG, and TAC by 2.48 %, 0.64 %, 2 %, and 3.04 %, correspondingly.

The dimensional compensation technology can achieve high dense metal parts with precise dimensions for binder jet 3D printing (BJ3DP). In this study, two models (cuboid and gear) of BJ3DP 316L stainless steel (BJ3DP316LSS) parts were established. Numerical simulation and experimental volume shrinkage of the BJ3DP316LSS sintered parts via dimensional compensations technology were investigated. When the dimensional compensation coefficient was set as 1.25, the BJ3DP316LSS cuboids and gears exhibited high densification as 99.6% and 99.4%, respectively. The experimental dimension deviation rates of cuboid and gear parts after dimensional compensations ranged from −3.56% to −0.15% and from 0.89% to 3.42%, respectively. Due to twinning-induced plasticity mechanism, the BJ3DP316LSS sintered gear part via dimensional compensation technology exhibited high hardness (∼139 HV), high yield strength (∼249 MPa), high ultimate tensile strength (∼546 MPa) and excellent elongation (∼62%), which are higher than those of the reported 316LSS samples.

The decreased ability of mature oligodendrocytes to produce myelin negatively affects remyelination in demyelinating diseases and aging, but the underlying mechanisms are incompletely understood. In the present study, we identify a mature oligodendrocyte-enriched transcriptional coregulator diabetes- and obesity-related gene (DOR)/tumor protein p53-inducible nuclear protein 2 (TP53INP2), downregulated in demyelinated lesions of donors with multiple sclerosis and in aged oligodendrocyte-lineage cells. Dorablation in mice of both sexes results in defective myelinogenesis and remyelination. Genomic occupancy in oligodendrocytes and transcriptome profiling of the optic nerves of wild-type and Dorconditional knockout mice reveal that DOR and SOX10 co-occupy enhancers of critical myelinogenesis-associated genes including Prr18, encoding an oligodendrocyte-enriched, proline-rich factor. We show that DOR targets regulatory elements of genes responsible for α-ketoglutarate biosynthesis in mature oligodendrocytes and is essential for α-ketoglutarate production and lipid biosynthesis. Supplementation with α-ketoglutarate restores oligodendrocyte-maturation defects in Dor-deficient adult mice and improves remyelination after lysolecithin-induced demyelination and cognitive function in 17-month-old wild-type mice. Our data suggest that activation of α-ketoglutarate metabolism in mature oligodendrocytes can promote myelin production during demyelination and aging.

The cooperative effects of main-group elements pave the way for novel chemical transformations. However, the potential of bimetallic complexes featuring the most abundant aluminum and silicon elements remains largely unexplored. In this study, we present the synthesis and characterization of bis(silylene)-stabilized aluminylene 2. The cooperation between aluminylene and silylene allows for the facile cleavage of the N–O bond in nitrosoarenes, producing an aluminum imide complex 4and tetracyclic oxazasilaalanes 5and 6, and also promotes the dearomatization of 2-methylquinoline, yielding a silylalane 7. In addition, 2is an effective precatalyst for the reductive coupling of nitrosoarenes to azoxyarenes. These results outline an approach for orchestrating aluminum and silicon cooperation to facilitate chemical bond activation.

2-D carbon-based materials are well-known for their broadband absorption properties for efficient solar energy conversion. However, their high reflectivity poses a challenge for achieving efficient omnidirectional light absorption. Inspired by the multilevel structures of the flower, a Graphene Microflower (GM) material with gradient refractive index surface was fabricated on polymer substrates using the UV-intense laser-induced phase explosion technique under the synergistic design of the photothermal Marangoni effect and the fluid instability principle. The refractive index gradient reduces light reflection and absorbs at least 96% of light at incident angles of 0–60° across the entire solar wavelength range (200–2500 nm). Over 90% absorption even at 75° angle of incidence. The light absorption is enhanced by the multiple interferometric phase cancelation and localized surface plasmon resonance, resulting in a steady-state temperature 60 °C higher than ambient conditions under one solar irradiation. The max rate of temperature rise can reach up to 62 °C s–1. The device is then integrated at the hot end of the temperature difference generator at high altitude to ensure continuous and efficient power generation, producing a steady-state power of 196 mW.

The railgun’s armature and rail are in high-speed sliding electrical contact, and there is a velocity skin effect regarding electromagnetic field distribution. Under this effect, current density is locally concentrated and is complex near the armature-rail interface. To ensure accuracy, mesh sizes need to be sufficiently fine around the armature-rail interface and the rail’s surface. Meanwhile, the Lagrangian method is commonly used to describe electromagnetic fields involving moving conductors, requiring a nearly structured mesh shape. Given the constraint of the mesh shape, implementing a meshing method that allows for appropriate and flexible mesh sizes is challenging, which may lead to inefficient and low-accuracy simulations. In this article, an efficient nonmatching meshing method is introduced to deliver structured meshes with appropriate and flexible sizes. Furthermore, the Nitsche mortar finite element method is used to ensure the continuity conditions across nonmatching interfaces in a weak sense. This method incorporates additional boundary integral terms into variational formulas. Current density results obtained using matching and nonmatching meshes were compared at velocities of 30 and 100 m/s, proving the correctness and efficiency of the nonmatching meshing method. In addition, current density distribution using nonmatching meshes was analyzed at velocities of 100, 500, and 1000 m/s, further demonstrating the feasibility of the abovementioned methods.

Tension force sensing of driving cables is essential for continuum surgical robots to ensure operational safety and enhance surgical quality and outcomes. This article presents a fiber Bragg grating (FBG)-based force sensor with high sensitivity and accuracy for cable tension measurement in continuum robots. The proposed sensor comprises a force-sensitive flexure with a trapezoidal-shaped beam and a suspended optical fiber inscribed with an FBG element. The force-sensing flexure is developed from a conceptual lever displacement amplification model, and its specific shape is determined by topology optimization to achieve high stiffness and compactness. It transforms the tension input into the flexure deformation, which is further amplified to stretch the FBG optical fiber. The optical fiber adopts a two-point suspended configuration to avoid chirping failure and enhance sensitivity, taking advantage of the flexure’s lever amplification capability. Finite element method (FEM)-based simulation has been implemented to investigate the proposed sensor’s performance, and structural size optimization has been conducted for optimal design parameters and enhanced sensitivity. The sensor prototype has been calibrated to demonstrate a high resolution of 10.1 mN within [0, 50 N] and excellent linearity with a slight error of 0.25% FS. Dynamic loading experiments have been conducted to verify the prototype’s high accuracy with a root-mean-square error (RMSE) of 0.33% FS. The cable tension measurement experiment integrated into a continuum robot has been implemented to validate the effectiveness of the proposed sensor.

Potassium content is an important standard for measuring tobacco quality. However, research on the mechanism of low potassium tolerance in cigar tobacco (Nicotiana tabacumL.), which differs from regular tobacco, did not draw researchers’ attention. This study evaluated the tolerance to low potassium of 23 cigar varieties by investigating 20 phenotypic and physiological indicators. Utilizing principal component analysis, membership function analysis, and cluster analysis, the research comprehensively identifies the tolerance of cigar tobacco varieties to low potassium. The results indicate that the tolerance to low potassium of cigar tobacco is complex and varies among different traits and varieties. The current study identified six crucial indicators for measuring tolerance to low potassium in cigar tobacco leaves, including plant height, maximum leaf area, leaf surface area, total root absorption area, and active root absorption area. Based on the comprehensive evaluation value D of the low potassium tolerance coefficient for these six indicators, the study classified the 23 cigar varieties into four categories: one low potassium-sensitive variety, five low potassium-low tolerance varieties, eleven low potassium-relative tolerant varieties, and six low potassium-tolerant varieties. These research outcomes significantly contribute to the elucidation of tolerance to low potassium in cigar tobacco, facilitating the evaluation, screening, and cultivation of cigar tobacco varieties resilient to low potassium conditions. Additionally, this study lays the foundation for exploring scientific issues such as how plants can effectively utilize potassium, the mechanism of potassium ions in plants, the improvement and development of cigar quality, the enhancement of potassium utilization efficiency, and conservation of limited potassium resources to ensure long-term potassium availability.

Purpose: This study aimed to investigate the effects of randomized, placebo-controlled trials involving the GLP-1 and glucagon receptor dual agonists, mazdutide, and cotadutide, on glycaemic control and body weight changes in individuals with type 2 diabetes mellitus (T2DM), obesity, or both. Methods: We conducted searches in Medline, PubMed, Scopus, the Cochrane database, and Web of Science up to March 5, 2024. The primary outcomes assessed were changes in HbA1c level and percentage changes in body weight from baseline (CFB). Results: Eleven studies and four unpublished trials were included. The pooled meta-analysis revealed a significant reduction in HbA1c (MD = −0.63%; 95% CI = [−0.82, −0.44]; P< 0.00001), fasting plasma glucose (MD = −1.71 mmol/L; 95% CI = [−2.31, −1.10]; P< 0.00001), and percentage change in body weight (MD = −4.16%; 95% CI = [−5.41, −2.92]; P< 0.00001). Safety analysis revealed no significant change in serious adverse events (OR = 1.03; 95% CI = [0.61, 1.75]; P= 0.91), but there were significantly higher odds of treatment-emergent adverse events (OR = 2.52; 95% CI = [1.92, 3.30]; P< 0.00001) and vomiting (OR = 6.05; 95% CI = [3.52, 10.40]; P< 0.00001). Conclusion: These results suggest that mazdutide and cotadutide are effective for glycaemic control and weight reduction in individuals with T2DM, obesity, or both.

Digital microfluidics (DMF) features programmed manipulation of fluids in multiple steps, making it a valuable tool for sample pretreatment. However, the integration of sample pretreatment with its downstream reaction and detection requires transferring droplets from the DMF device to the outside world. To address this issue, the present study developed a modified DMF device that allows automated droplet ejection out of the chip, facilitated by a tailor-designed interface. A double-layered DMF microchip with an oil-filled medium was flipped over, with a liquid infusion port and a liquid expulsion port accommodated on the top working PCB plate and the bottom grounded ITO plate, respectively, to facilitate chip-to-world delivery of droplets. Using chemiluminescent immunoassay (CLIA) as an illustrative application, the sample pretreatment was programmed on the DMF device, and CLIA droplets were ejected from the chip for signal reading. In our workflow, CLIA droplets can be ejected from the DMF device through the chip-to-world interface, freeing up otherwise occupied electrodes for more sample pretreatment and enabling streamlined droplet microreactions and batch-mode operation for bioanalysis. Integrated with these interfacing portals, the DMF system achieved a single-channel throughput of 17 samples per hour, which can be further upscaled for more productive applications by parallelizing the DMF modules. The results of this study demonstrate that the droplet ejection function that is innovated in a DMF sample pretreatment microsystem can significantly improve analytical throughput, providing an approach to establishing an automated but decentralized biochemical sample preparation workstation for large-scale and continuous bioanalysis.

The mechanism of biological effects of environmental electromagnetic radiation is still not completely clear. The chelation of biological small molecule peptides with metal ions plays a very important role in human metabolism. In this paper, a special experimental system was designed to measure the conductivity of carnosine and zinc chloride mixed aqueous solutions under different concentration ratios, microwave powers, and temperatures. The experimental results show that, first, different concentration ratios can alter the conductivity change rate of the mixed aqueous solution. The conductivity of the solution always increases under microwave irradiation at a concentration ratio of 1:1. However, the conductivity is reduced by −0.04% with a 1:5 concentration ratio and 6 W microwave power at 10 °C. Second, temperature can alter the conductivity change rate of the aqueous mixture. The higher the temperature, the smaller the conductivity change rate. Third, different microwave powers can alter the conductivity change rate of the mixed aqueous solution. In general, the conductivity change rate increases with an increase in microwave power. Experimentally observed reduction of the conductivity change rate in carnosine and zinc chloride aqueous solution under low microwave power and low temperature indicates that microwaves do affect the chelation of carnosine with zinc chloride. This work provides a new perspective for the mechanism of explanation of microwave biological effects.

Increasing planting density is a key strategy for enhancing maize yields1–3. An ideotype for dense planting requires a ‘smart canopy’ with leaf angles at different canopy layers differentially optimized to maximize light interception and photosynthesis4–6, among other features. Here we identified leaf angle architecture of smart canopy 1(lac1), a natural mutant with upright upper leaves, less erect middle leaves and relatively flat lower leaves. lac1has improved photosynthetic capacity and attenuated responses to shade under dense planting. lac1encodes a brassinosteroid C-22 hydroxylase that predominantly regulates upper leaf angle. Phytochrome A photoreceptors accumulate in shade and interact with the transcription factor RAVL1 to promote its degradation via the 26S proteasome, thereby inhibiting activation of lac1by RAVL1 and decreasing brassinosteroid levels. This ultimately decreases upper leaf angle in dense fields. Large-scale field trials demonstrate that lac1boosts maize yields under high planting densities. To quickly introduce lac1into breeding germplasm, we transformed a haploid inducer and recovered homozygous lac1edits from 20 diverse inbred lines. The tested doubled haploids uniformly acquired smart-canopy-like plant architecture. We provide an important target and an accelerated strategy for developing high-density-tolerant cultivars, with lac1serving as a genetic chassis for further engineering of a smart canopy in maize.

Fiber-integrated micro/nanostructures play a crucial role in modern industry, mainly owing to their compact size, high sensitivity, and resistance to electromagnetic interference. However, the three-dimensional manufacturing of fiber-tip functional structures beyond organic polymers remains challenging. It is essential to construct fiber-integrated inorganic silica with designed functional nanostructures for microsystem applications. Here, we develop a strategy for the 3D nanolithography of fiber-integrated silica from hybrid organic–inorganic materials by ultrafast laser-induced multiphoton absorption. Without silica nanoparticles and polymer additives, the acrylate-functionalized precursors can be locally cross-linked through a nonlinear effect. Followed by annealing at low temperature, the as-printed micro/nanostructures are transformed to high-quality silica with sub-100 nm resolution. Silica microcantilever probes and microtoroid resonators are directly integrated onto the optical fiber, showing strong thermal stability and quality factors. This work provides a promising strategy for fabricating desired fiber-tip silica micro/nanostructures, which is helpful for the development of integrated functional device applications.

Inspired by providing reliable communications for high-mobility scenarios, in this letter, we investigate the channel estimation and signal detection in integrated sensing and communication (ISAC) systems based on the orthogonal delay-Doppler multiplexing (ODDM) modulation, which consists of a pulse-train that can achieve the orthogonality with respect to the resolution of the delay-Doppler (DD) plane. To enhance the communication performance in the ODDM-based ISAC systems, we first propose a low-complexity approximation algorithm for channel estimation, which addresses the challenge of the high complexity from high resolution in the ODDM modulation, and achieves performance close to that of the maximum likelihood estimator scheme. Then, we employ the orthogonal approximate message-passing scheme to detect the symbols in the communication process based on the estimated channel information. Finally, simulation results show that the detection performance of ODDM is better than other multi-carrier modulation schemes. Specifically, the ODDM outperforms the orthogonal time frequency space scheme by 2.3 dB when the bit error ratio is $10^{-6}$ .

In this work, hollow tubular CoMo unsupported catalysts were prepared using cotton as a hard template with different Co and Mo molar ratios. The techniques, such as thermogravimetric analysis (TGA), X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and low-temperature N2adsorption–desorption were employed to characterize the physicochemical properties of the CoMo-x catalysts. The HDS activity evaluation results demonstrated that the thiophene conversion over the CoMo-3 catalyst was 95.82% at 1 MPa and 360 °C, which could be ascribed to the highly dispersed MoS2. Although the BET surface area of all catalysts was as low as 5–12 m2g−1, the hollow tubular structure could enhance the diffusion of thiophene in the catalyst pore channel and elevate the exposure degree of MoS2. This study provides a new idea for using low-cost hard templates to prepare highly active unsupported HDS catalysts.

Data collection and processing timely is crucial for mobile crowd integrated sensing, communication, and computation (ISCC) systems with various applications such as smart home and connected cars, which requires numerous integrated sensing and communication (ISAC) devices to sense the targets and offload the data to the base station (BS) for further processing. However, as the number of ISAC devices grows, there exists intensive interactions among ISAC devices in the processes of data collection and processing since they share the common network resources. In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game (MFG). Specifically, to handle the complex interactions among large-scale ISAC devices, we first utilize the MFG method to transform the influence from other ISAC devices into the mean field term and derive the Fokker-Planck-Kolmogorov equation, which models the evolution of the system state. Then, we derive the cost function based on the mean field term and reformulate the waveform precoding design problem. Next, we utilize the G-prox primal-dual hybrid gradient algorithm to solve the reformulated problem and analyze the computational complexity of the proposed algorithm. Finally, simulation results demonstrate that the proposed algorithm can solve the interactions among large-scale ISAC devices effectively in the ISCC process. In addition, compared with other baselines, the proposed waveform precoding design algorithm has advantages in improving communication performance and reducing cost function.

This research study deals with the heat prediction of the battery in electric vehicles. An all-electric vehicle is considered in this study. The FTP-75, NEDC, and WLTC standard driving cycles are used to assess the performance of the electric vehicle battery. Additionally, the actual Munich driving travel cycle was employed in the simulation. The parameters that determine the performance of the vehicle, such as the battery's state of charge (SOC), energy consumption, and operating temperature, are evaluated. The results indicate that raising the gradients increases the temperature and the heat output from the battery. In addition, Munich's driving cycle increased driving time and rapid accelerations have raised temperatures and increased heat generation. The results showed that an increase in the road slope of 5 % results in an increase in the battery temperature of 3 % and a reduction in the SOC of the battery for 20 %. In addition, results showed that for a travel path with an average road slope of 5°, the battery temperature increases about 7 0C which requires more thermal management using hybrid cooling.

Edge network slicing promises better utilization of network resources by dynamically allocating resources on demand. However, addressing the imbalance between slice resources and user demands becomes challenging when complex user behaviors lead to bursty traffic within the edge network. Hence, we propose a comprehensive dynamic slice strategy with two coupled sub-strategies (i) bursty-sensitive slice resource coordination and (ii) proactive demand resource matching to find an optimal balance. For obtaining stable strategies, the edge network with bursty traffic is formulated as a bi-level Lyapunov optimization problem. Then we propose a resource allocation and request redirection (RA-RR) algorithm with polynomial complexity by introducing deep reinforcement learning to guarantee real-time. Specifically, two agents are trained to solve two sub-strategies, and the Lyapunov drift-plus-penalty function is used as the reward to keep queues stable. RA-RR is responsive to fluctuations in demand and realizes an efficient interaction of coupled decision-making. Moreover, a training method based on alternating optimization is designed to ensure convergence of the RA-RR algorithm. Experiments demonstrate that the proposal can maximize network revenue while ensuring the stability of slice services when edge traffic bursts, and has an average improvement of 20.4% compared with comparisons.

Compared with terrestrial networks, unmanned aerial vehicles (UAVs) have the characteristics of flexible deployment and strong adaptability, which are an important supplement to intelligent transportation systems (ITS). In this paper, we focus on the multi-UAV network area coverage problem (ACP) which require intelligent UAVs long-term trajectory decisions in the complex and scalable network environment. Multi-agent deep reinforcement learning (DRL) has recently emerged as an effective tool for solving long-term decisions problems. However, since the input dimension of multi-layer perceptron (MLP)-based deep neural network (DNN) is fixed, it is difficult for standard DNN to adapt to a variable number of UAVs and network users. Therefore, we combine Transformer with DRL to meet the scalability of the network and propose a Transformer-based deep multi-agent reinforcement learning (T-MARL) algorithm. Transformer can adapt to variable input dimensions and extract important information from complex network states by attention module. In our research, we find that random initialization of Transformer may cause DRL training failure, so we propose a baseline-assisted pre-training scheme. This scheme can quickly provide an initial policy model for UAVs based on imitation learning, and use the temporal-difference(1) algorithm to initialize policy evaluation network. Finally, based on parameter sharing, T-MARL is applicable to any standard DRL algorithm and supports expansion on networks of different sizes. Experimental results show that T-MARL can make UAVs have cooperative behaviors and perform outstandingly on ACP.

A comprehensive understanding of blast load information is crucial for evaluating structural response. Machine learning has demonstrated its efficiency in predicting blast loads. However, the high costs associated with acquiring blast load data limit the development of machine learning. A novel method, PCA-TANN, is proposed for predicting blast load time series on structures and improving the problem of insufficient training data by integrating transfer learning concepts and combining artificial neural networks (ANN) with principal component analysis (PCA). The main idea of this model involves preprocessing the time series data using PCA to extract essential features, employing artificial neural networks based on transfer learning (TANN) for knowledge transfer from the previous task to the new task, and integrating a few preprocessed data of the new task for training. The blast load data obtained from a single square column is utilized to assess the performance of PCA-TANN and PCA-ANN models. The findings demonstrate that in situations with limited data availability, PCA-TANN reasonably replicates the blast load characteristic, resulting in accurate time series predictions of overpressure with R2scores exceeding 0.85. This performance significantly surpasses that of the used PCA-ANN model, which achieves R2scores below 0.4.

Activated carbon (AC) treated with nitric acid (HNO3) and hydrogen peroxide (H2O2) was prepared for the study of the effect of oxygen-containing functional groups on the adsorptive removal of organochlorines from oil. The adsorption performance and physicochemical properties of the adsorbents were tested and analyzed. The results show that the HNO3–H2O2comodified AC (NPAC) presented the best adsorptive efficiency, especially for the removal of aromatic chlorides. It has been found that the surface chemical groups and the physical texture of AC play major roles in the adsorption performance of organic chlorides. The potential of activated carbon (NPAC) treated with nitric acid and hydrogen peroxide in removing organic chlorine from oil was comprehensively evaluated through experimental data combined with mathematical models of kinetics and equilibrium. The adsorption process conforms to the Freundlich isotherm equation, which proves that there is multilayer adsorption in the adsorption process, and 1,2-dichlorobenzene (DCB) is more easily adsorbed by modified AC. The kinetic results indicate that there is chemical adsorption during the adsorption process, and the diffusion process of organic chlorides in AC is influenced by several factors.

This study aims to prepare a stable crystal-co-agglomeration (CCA) process for combining the antibiotics trimethoprim (TMP) and the anti-inflammatory niflumic acid (NFA) as well as the enhancement of powder properties. A novel TMP–NFA salt monohydrate was synthesized and characterized through multitechniques for the first time. Subsequently, the antibacterial effectiveness of TMP–NFA salt monohydrate against Staphylococcus aureusand Shigella flexneriwas found to be improved (P< 0.05) compared with that of TMP at certain concentrations, achieving a synergistic effect of TMP and NFA. Based on these findings, spherical agglomerates of TMP–NFA salt monohydrate with superior powder properties including improved flowability (Carr’s index decreased by 37.5% and Hausner’s ratio decreased by 16.3%) and tabletability were produced using an efficient CCA process. Hence, the optimized spherical agglomerates of the drug–drug salt technique provide a promising approach to simultaneously enhance the powder properties and synergistic effect of active pharmaceutical ingredients. The development of such drug–drug salts holds great potential for advancing pharmaceutical formulations and therapeutic outcomes.

The link between gut microbiota (GM) and primary osteoporosis has garnered substantial attention. In this study, we conducted a systematic review encompassing 10 studies that employed high-throughput sequencing methodologies. Notably, the taxonomic profiles exhibited pronounced heterogeneity across reviewed studies. A combined analysis of mechanistical studies screened out potential targets for osteoporosis prevention and treatment. We appraised the limitations in previous studies, and proposed suggestions for optimizing sequencing techniques and experimental designs. Larger-size longitudinal cohorts based on multi-omics and extended phenotypes are recommended to comprehensively characterize the involvement of GM in the pathogenesis of osteoporosis.

Dissolved organic matter (DOM) not only reflects the biochemical transformation of organic matter during the composting process, but also evaluates the humification degree of composting. However, most research focuses on laboratory composting of DOM. In this study, the stability and maturity of DOM in large-scale industrial sludge compost were studied by basic physical, chemical parameters and spectral technology. The results showed that the initial temperature of compost increased rapidly, the high temperature period was longer, and the pH value first increased and then stabilized. The increase in the UV–Vis characteristic values SUVA280and SUVA254and the decrease in E250/E365and SRindicated an increased humus content. Fourier transform infrared spectroscopy (FTIR) analysis showed that easily degradable substances such as polysaccharides and carbohydrates were decomposed into humic substances. The absorption rate of 1645 cm−1/1103 cm−1, 1645 cm−1/1400 cm−1, and 1645 cm−1/2930 cm−1confirmed that humic substances increased with the decomposition of polysaccharide compounds and alcohols. Fluorescence excitation-emission matrix spectroscopy (EEM) combined with parallel factor analysis (PFA) showed a gradual decrease in the C3 fraction representing the protein, with decomposition and conversion to humic and xanthate fractions, which was also confirmed by the four-component model (Fmax), the fluorescence region integral (FRI), and the humification index (HIX). This study uses a variety of spectral techniques combined with basic physicochemical parameters to assess the humification of industrial sludge composting, which provides a reference for guiding large-scale composting.

The study combined experimental and numerical simulation methods to investigate the distribution of residual stress in laser composite welding of dissimilar aluminum alloys 6063 and 5083. Using the Simufact Welding simulation software and a combined volume heat source, the distribution patterns of transverse and longitudinal residual stress fields at different welding speeds were analyzed. The blind hole method was used to test the residual stress after welding. The results showed that the use of a combined volume heat source model for heat source calibration yielded ideal results, and the thermal cycle curve was basically consistent with the simulation and experimental results of residual stress. As the welding speed decreased, the longitudinal residual stress of the parent material on both sides significantly decreased, while the transverse residual stress increased. The majority of tensile and compressive stresses on both sides of the 5083 and 6063 aluminum alloys decreased with a decrease in welding speed. The Von Mises equivalent stress indicated that the cooling process is the main stage for residual stress generation. When the welding speed decreased, the peak value of longitudinal residual stress on the 6063 aluminum alloy side decreased from 230 to 90 MPa, while that on the 5083 aluminum alloy side decreased from 304 to 150 MPa. These findings provide an important basis for controlling and reducing the residual stress and deformation of dissimilar aluminum alloy laser arc composite welded components.

ABSTRACT In this study, the early-age hydration process of cement paste is characterized using the rate of change in the impedance measured by electrochemical impedance spectroscopy (EIS). To effectively describe [...]

ABSTRACT This paper discusses the feasibility of reusing steel slag as mineral filler in an asphalt mixture. Steel slag aggregate was first crushed and ground into filler with particles smaller [...]

Supported vanadium oxide (VOX) catalysts are promising candidates for propane dehydrogenation (PDH) due to their high catalytic activity, low cost, and environmentally benign characteristics. Herein, vanadium-based catalysts supported on hierarchical SSZ-13 zeolites with V loadings ranging from 1 to 12 wt % were prepared and evaluated in PDH reaction. The characterization results show that various kinds of VOXspecies are formed on the SSZ-13 zeolites, and the polymerization degree of VOXspecies continually increases with rising V loading. The 5V/SSZ catalyst possessing abundant low-polymerized VOXspecies with a large ratio of active V3+and V4+exhibits superior catalytic activity with 19.6% propane conversion and 91.3% propylene selectivity as well as good regeneration stability. The nano-hierarchical structure of SSZ-13 zeolite is also found to play an important role in enhancing PDH activity due to the suppressed coke deposition, improved mass transfer, and enhanced accessibility of propane molecules to the active sites.

The state of austenite grains has an essential role in the microstructure transformation and mechanical properties of alloy steel during heat treatment or subsequent hot working. The initial microstructure of alloy steel is inseparable from the nucleation and growth of austenite grains. Hence, in this work, high carbon low alloy steel with the initial microstructure of lamellar pearlite and grain boundary cementite was selected, and the effect of cementite dissolution on austenite grain growth was analyzed from the perspective of the phase transition dilatometer curve. Three nucleation sites in high carbon low alloy steel were found by multi-perspective characterization, forming two types of austenite grains: pearlite-austenite grain and cementite-austenite grain. Under cementite pinning and solute drag effect, the growth of the prior austenite grains was retarded during the isothermal temperatures process. The average size of prior austenite grains increased only from 65.1 μm (1050 °C) to 69.8 μm (1150 °C). This work provides a foundation for optimizing the prior austenite grains state by utilizing grain boundary cementite dissolution.

Core-shell structured particles are potential reinforcement agents for metal matrix composites. In this work, aluminum matrix composites reinforced with core-shell structured Al35Ti15Cu10Mn20Cr20high-entropy alloy (HEA) particles were fabricated by spark plasma sintering (SPS) and high-temperature diffusion post-treatment. Dynamic compression behavior and adiabatic shear failure mechanism in the composites were investigated by split Hopkinson pressure bar (SHPB), scanning electron microscopy and transmission electron microscopy. Results showed that the shell thickness of the core-shell particles ranged from 0.4 to 1.6 μm, which were formed by thermal diffusion between HEA core and aluminum alloy. The 30 vol% (Al35Ti15Cu10Mn20Cr20)p/2024Al composite showed a high compressive strength (594 MPa) and strain-to-failure (26.7 %) under quasi-static compression, as well as a high flow stress (602 MPa) and strain-to-failure (45.3 %) under dynamic compression, which are superior to common ceramic particles reinforced Al matrix composites. The (Al35Ti15Cu10Mn20Cr20)p/Al composites with ∼20–40 vol% core-shell HEA particles failed as bulging, 45° shearing or splitting when compressed at ∼1000-3000 s−1. Micro-damages in these composites were due to microcracks originated from the HEA core, while propagation of cracks was effectively restrained by the shell. Deformation bands and phase transformation (melting of aluminum) were both observed in the composites under dynamic loads. Chain-shaped deformation bands were formed by the fragmentation and rearrangement of the core-shell particles, instead of the recrystallized structure of metal matrix in particle-reinforced Al matrix composites reported previously. The formation of molten Al phase transformation bands was owing to shear localization and significant adiabatic temperature rise under dynamic compression.

In order to improve the corrosion resistance of welded joints, X90 pipeline steel joints were subjected to post-weld heat treatment at 610 °C, 640 °C, and 670 °C (holding time was 1 h). Through electrochemical corrosion and full immersion corrosion experiments, the corrosion resistance of welded joints under various conditions was tested, and the surface morphology and corrosion products of the corroded samples were analyzed by scanning electron microscopy and X-ray diffraction analysis. The experimental results show that the corrosion products of X90 pipeline steel welded joints in simulated soil solution mainly include Fe(OH)3, γ-FeOOH, α-Fe2O3, γ-Fe2O3, and a small amount of Fe3O4, FeCl3and Fe. After heat treatment at 610 °C, the corrosion current density of the parent metal, heat-affected zone, and weld metal of the joint changes from 1.081, 2.889, 2.079 (×10−5A cm−2) to 0.977, 2.211, 1.810 (×10−5A cm−2), respectively, the corrosion resistance is improved.

• We fabricate vertical NiSe 2 –MoSe 2 heterostructures on carbon cloth (CC). • The heterostructure displays a low overpotential of 98 mV for HER at 10 mA cm−2. • The NiSe 2 –MoSe 2 /CC||NiSe 2 –MoSe 2 /CC features a voltage of 1.59 V at 10 mA cm2. • The heterostructures improve the conductivity and hydrophilicity. • The heterostructures also optimize the Gibbs free energy of hydrogen adsorption. Despite the rapid advances in electrocatalysts based on two-dimensional (2D) transition metal dichalcogenides (TMDs) materials, they are subject to serious aggregation, poor conductivity and the presence of inactive basal planes. Herein, we have successfully demonstrated the in-situ construction of NiSe 2 –MoSe 2 heterostructure arrays on carbon cloth (NiSe 2 –MoSe 2 /CC) by a facile two-step hydrothermal process. The presence of the synergistic effect in the heterostructures effectively optimizes the poor conductivity and hydrophilicity, and thus enables fast electron transfer, leading to enhanced electrochemical reaction. Furthermore, density functional theory calculations reveal that the electrons redistribution at the heterojunction interface and the reduced Gibbs free energy of hydrogen adsorption for hydrogen evolution reaction (HER)/the Gibbs free energy change value of rate-determining step for oxygen evolution reaction (OER), thus enhancing the HER/OER catalytic activity. Importantly, the device displays a good performance with a low overpotential of 98 and 310 mV for HER and OER, respectively, and a low cell voltage of 1.59 V for its corresponding electrolyzer (10 mA cm−2). This work presents the high-performance water splitting of bifunctional electrocatalysts based on 2D TMDs materials and offers a novel design concept of interface engineering. [Display omitted] Vertical NiSe 2 –MoSe 2 nanoheterostructure arrays supported on carbon cloth (NiSe 2 –MoSe 2 /CC) were in-situ constructed and further used as efficient bifunctional electrocatalyst for alkaline water electrolysis. The interface engineering and the synergistic effect in the heterostructures can effectively optimize the conductivity, hydrophilicity and Gibbs free energy of hydrogen adsorption, leading to accelerated reaction kinetics. [ABSTRACT FROM AUTHOR]

The photocatalytic activity of g-C3N4can be enhanced by improving photoinduced carrier separation and exposing sufficient reactive sites. In this study, we synthesized B-doped porous tubular g-C3N4(BCNT) using a H3BO3-assisted supramolecular self-template method, wherein H3BO3helped in B-doping, building a porous structure, and maintaining one-dimensional nanotubes. The tubular structure had an ultrathin tube wall and large aspect ratio, which are conducive to the directional transmission and separation of photogenerated carriers; moreover, the abundant pore structure of the tube wall could fully expose the reactive sites. The introduction of B and the cyano group modulated the bandgap of g-C3N4and elevated the position of the conduction band, thus enhancing the photoreduction ability and effectively improving the hydrogen evolution performance. Consequently, the hydrogen evolution of BCNT-2 (220.8, 53.2 μmol·h–1) was 1.82 and 1.54 times that of ultrathin g-C3N4nanosheets (CNN, 121.3, 34.6 μmol·h–1) under simulated sunlight and LED lamp irradiation, respectively. Thus, this work provides in-depth insights into the rational design of one-dimensional g-C3N4nanotubes with high hydrogen evolution activity under visible irradiation.

ABSTRACT The layered double hydroxides (LDHs) has been widely used as the anti-aging modifier to improve the anti aging performance of an asphalt binder, but for the different chemical and [...]